Quantification of nucleic acids is important in a number of fields ranging from molecular biology and genetic research to diagnostics and pathogen detection. When sufficient amounts of nucleic acids are present blot-techniques can be applied for quantification. However, the limited sensitivity of these techniques prevents their use in a number of cases.
Quantitative PCR methods developed in the recent past provided tools for analysis in cases where much higher sensitivity was required. These techniques are based on the fact that during PCR amplification the amount of product grows exponentially and, thus, the amount of product obtained after a small number of cycles can be detected by conventional means, (e.g., fluorescent detection). Further, in principle, the amount of product that was present initially, i.e., at the beginning of the amplification, can be determined from the amount of product obtained at the end of the amplification if the number of amplification cycles is known.
A typical plot of PCR product formed over the course of an amplification reaction reveals four different phases of the amplification process (see FIG. 1): (1) the ground phase (GP) where the fluorescent signal is dominated by background fluorescent and noise; (2) the exponential phase (EP) where the signal from the PCR product rises above ground level and increases exponentially; (3) the log-linear phase (LP) where the signal increases at a less than exponential rate due to decreasing amplification efficiency caused by such factors as the consumption of PCR reagents and the degradation of detection probes; (4) the plateau phase (PP) with marginal rise of the signal due to an increasing slowdown and eventual stop of the amplification reaction.
At present, however, no physical models are available that describe the development of the signals detected during the PCR process in a realistic fashion. Therefore, current methods for the quantification of nucleic acids require performing calibration steps involving performing the same PCR reactions to reference samples with known concentrations of standard and/or comparative nucleic acid sequences. Often times, the nucleic acid sequences used as standards are well known housekeeping genes. Briefly outlined, in practice, target nucleic acid sequences as well as standard and/or comparative samples are subjected to PCR under defined reaction conditions and formation of the PCR product, also called an amplicon, is monitored over the course of the amplification process. Detection of PCR product is achieved, for example, by means of fluorescently labeled hybridization probes or by means of deoxyribonucleic acid (“DNA”)-intercalating fluorescent dyes that detect double stranded PCR product. The number of amplification cycles necessary to obtain a particular fluorescence threshold-level, designated as Ct-values, are determined, and the Ct-value of the target is compared to the Ct-values of the samples of a dilution series of a nucleic acid standard with known concentrations (absolute quantification). In order to determine the absolute quantity of the target a standard curve is constructed from the Ct-values of the standard samples and used to determine the initial concentration of the target. Alternatively, the Ct-value of the target is compared to the Ct-value of a single comparative nucleic acid of interest (relative quantification). In this case, the ratio of the Ct-values of target and comparative samples is determined and used to assess the ratio of the initial quantities of target and comparative nucleic acid sequences.
In general, the development of new methods for nucleic acid quantification is confronted with a number of challenges some of which stem from the fact that several applications require complete automation as will be discussed in more detail below and some of which are related to the calibration process.
The calibration process required by methods for nucleic acid quantification employing Ct-value determination introduces a number of potential limitations. First of all, the examination of standard samples requires additional experimental effort and resources. Secondly, these methods are based on the assumption that the amplification efficiency in standard and target samples is the same. Importantly, this assumption is not generally correct and, thus, provides a source of inaccuracy.
Additional challenges in the field of quantitative PCR are related to the growing need to analyze large numbers of samples in short intervals of time. As an ever increasing range of applications for quantitative PCR requires analysis of very large numbers of samples in a high-throughput fashion, e.g. in clinical practice, it is necessary to develop quantitative PCR methods that can be completely automated and require very little or no human interaction. This is of crucial importance in some cases as high throughput applications (e.g. in clinical practice) simply cannot be conducted in the required short periods of time if human interaction is required.
An additional benefit that could be realized with such automated methods would be an improved comparability of analytical data between different labs currently employing widely differing laboratory protocols for quantitative PCR. The issue is of paramount importance in view of an increasing number of labs using quantitative PCR techniques for basic research. Establishing an automated method as an objective reference for quantification experiments would drastically benefit these research efforts by enhancing consistency from lab to lab.
Two methods employing Ct-value determination are currently available for nucleic acid quantification that, in principle, appear suitable for complete automation: the second derivative maximum method and the sigmoidal curve-fitting method. For the second derivative maximum method the maximum of the second derivative of the amplification curve is determined numerically. The corresponding cycle is assumed to represent the end of the exponential growth phase, where the reaction begins to slow down to linear growth. This cycle number is used, analogously to Ct, for determining the quantity of the target. For the sigmoidal curve-fitting method a sigmoid function is modeled upon the amplification curve. The cycle number corresponding to the inflection point of the curve can be obtained from the model and is used, analogously to Ct, for determining the quantity of the target. The second derivative maximum method and the sigmoidal curve-fitting method, however, have been found to be of limited use for applications requiring high sensitivity (J D Durtschi et al., Analytical Biochemistry, 361 (2007), pp. 55-64). Furthermore, both of these methods require calibration steps. In addition the sigmoid function modeled upon the amplification curve in the process of employing the sigmoidal curve-fitting method is greatly idealized and can, by no means be regarded as a physical model describing the development of the signals detected during the PCR process.